From 30cc445179b8b51b854f2b7d254f549ce92e761e Mon Sep 17 00:00:00 2001
From: Tim O'Donnell <timodonnell@gmail.com>
Date: Thu, 5 Dec 2019 16:50:28 -0500
Subject: [PATCH] Implement
Class1AffinityNeuralNetwork.copy_weights_to_affinity_model
---
mhcflurry/class1_neural_network.py | 1 -
.../class1_presentation_neural_network.py | 40 ++++++++++++++-----
setup.py | 2 +-
test/test_class1_presentation_predictor.py | 23 ++++++++++-
4 files changed, 51 insertions(+), 15 deletions(-)
diff --git a/mhcflurry/class1_neural_network.py b/mhcflurry/class1_neural_network.py
index 1b5f2eca..4829449e 100644
--- a/mhcflurry/class1_neural_network.py
+++ b/mhcflurry/class1_neural_network.py
@@ -1384,7 +1384,6 @@ class Class1NeuralNetwork(object):
numpy.zeros(shape=(0,) + existing_weights_shape[1:]),
force_surgery=True)
-
def set_allele_representations(self, allele_representations, force_surgery=False):
"""
Set the allele representations in use by this model. This means mutating
diff --git a/mhcflurry/class1_presentation_neural_network.py b/mhcflurry/class1_presentation_neural_network.py
index 503fa4bc..d3a69e93 100644
--- a/mhcflurry/class1_presentation_neural_network.py
+++ b/mhcflurry/class1_presentation_neural_network.py
@@ -89,7 +89,7 @@ class Class1PresentationNeuralNetwork(object):
self.fit_info = []
self.allele_representation_hash = None
- def load_from_class1_neural_network(self, class1_neural_network):
+ def load_from_class1_neural_network(self, model):
import keras.backend as K
from keras.layers import (
Input,
@@ -107,11 +107,11 @@ class Class1PresentationNeuralNetwork(object):
from keras.models import Model
from keras.initializers import Zeros
- if isinstance(class1_neural_network, Class1NeuralNetwork):
- class1_neural_network = class1_neural_network.network()
+ assert isinstance(model, Class1NeuralNetwork), model
+ affinity_network = model.network()
peptide_shape = tuple(
- int(x) for x in K.int_shape(class1_neural_network.inputs[0])[1:])
+ int(x) for x in K.int_shape(affinity_network.inputs[0])[1:])
input_alleles = Input(
shape=(self.hyperparameters['max_alleles'],), name="allele")
@@ -138,7 +138,7 @@ class Class1PresentationNeuralNetwork(object):
[peptides_repeated, allele_flat], name="allele_peptide_merged")
layer_names = [
- layer.name for layer in class1_neural_network.layers
+ layer.name for layer in affinity_network.layers
]
pan_allele_layer_initial_names = [
@@ -153,7 +153,7 @@ class Class1PresentationNeuralNetwork(object):
assert startswith(
layer_names, pan_allele_layer_initial_names), layer_names
- layers = class1_neural_network.layers[
+ layers = affinity_network.layers[
pan_allele_layer_initial_names.index(
"allele_peptide_merged") + 1:
]
@@ -270,7 +270,13 @@ class Class1PresentationNeuralNetwork(object):
],
name="presentation",
)
- self.network.summary()
+
+ def copy_weights_to_affinity_model(self, model):
+ # We assume that the other model's layers are a prefix of ours.
+ self.clear_allele_representations()
+ model.clear_allele_representations()
+ model.network().set_weights(
+ self.get_weights()[:len(model.get_weights())])
def peptides_to_network_input(self, peptides):
"""
@@ -659,15 +665,27 @@ class Class1PresentationNeuralNetwork(object):
self.network.predict(x_dict, batch_size=batch_size))
return predictions
- def set_allele_representations(self, allele_representations):
+ def clear_allele_representations(self):
+ """
+ Set allele representations to an empty array. Useful before saving to
+ save a smaller version of the model.
+ """
+ layer = self.network.get_layer("allele_representation")
+ existing_weights_shape = (layer.input_dim, layer.output_dim)
+ self.set_allele_representations(
+ numpy.zeros(shape=(0,) + existing_weights_shape[1:]),
+ force_surgery=True)
+
+ def set_allele_representations(self, allele_representations, force_surgery=False):
"""
"""
from keras.models import clone_model
import keras.backend as K
import tensorflow as tf
- reshaped = allele_representations.reshape(
- (allele_representations.shape[0], -1))
+ reshaped = allele_representations.reshape((
+ allele_representations.shape[0],
+ numpy.product(allele_representations.shape[1:])))
original_model = self.network
layer = original_model.get_layer("allele_representation")
@@ -677,7 +695,7 @@ class Class1PresentationNeuralNetwork(object):
# the allele sequences) are allowed.
assert existing_weights_shape[1:] == reshaped.shape[1:]
- if existing_weights_shape[0] > reshaped.shape[0]:
+ if existing_weights_shape[0] > reshaped.shape[0] and not force_surgery:
# Extend with NaNs so we can avoid having to reshape the weights
# matrix, which is expensive.
reshaped = numpy.append(
diff --git a/setup.py b/setup.py
index 78809cd4..9275775e 100644
--- a/setup.py
+++ b/setup.py
@@ -53,7 +53,7 @@ if __name__ == '__main__':
'pandas>=0.20.3',
'Keras>=2.2.5',
'appdirs',
- 'tensorflow>=1.1.0,<2.0.0',
+ 'tensorflow>=1.15.0,<2.0.0',
'scikit-learn',
'mhcnames',
'pyyaml',
diff --git a/test/test_class1_presentation_predictor.py b/test/test_class1_presentation_predictor.py
index b54bee2a..eeb574d3 100644
--- a/test/test_class1_presentation_predictor.py
+++ b/test/test_class1_presentation_predictor.py
@@ -99,7 +99,7 @@ def test_basic():
optimizer="adam",
random_negative_rate=0.0,
random_negative_constant=0,
- max_epochs=100,
+ max_epochs=25,
learning_rate=0.001,
batch_generator_batch_size=256)
presentation_network.load_from_class1_neural_network(affinity_network)
@@ -190,6 +190,9 @@ def test_basic():
train_df["updated_score"] = new_predictor.predict(
train_df.peptide.values,
alleles=["HLA-A*02:20"])
+ train_df["updated_affinity"] = new_predictor.predict_to_dataframe(
+ train_df.peptide.values,
+ alleles=["HLA-A*02:20"]).affinity.values
train_df["score_diff"] = train_df.updated_score - train_df.original_score
mean_change = train_df.groupby("label").score_diff.mean()
print("Mean change:")
@@ -203,6 +206,22 @@ def test_basic():
train_df.pre_train_affinity_prediction.values,
train_df.post_train_affinity_prediction.values)
+ (affinity_model,) = affinity_predictor.class1_pan_allele_models
+ model.copy_weights_to_affinity_model(affinity_model)
+ train_df["post_copy_weights_prediction"] = affinity_predictor.predict(
+ train_df.peptide.values, alleles=train_df.allele.values)
+ assert_allclose(
+ train_df.updated_affinity.values,
+ train_df.post_copy_weights_prediction.values,
+ rtol=1e-5)
+ train_df["affinity_diff"] = (
+ train_df.post_copy_weights_prediction -
+ train_df.post_train_affinity_prediction)
+ median_affinity_change = train_df.groupby("label").affinity_diff.median()
+ print("Median affinity change:")
+ print(median_affinity_change)
+ assert_less(median_affinity_change[1.0], median_affinity_change[0.0])
+
def scramble_peptide(peptide):
lst = list(peptide)
@@ -235,7 +254,7 @@ def evaluate_loss(loss, y_true, y_pred):
raise ValueError("Unsupported backend: %s" % K.backend())
-def test_loss():
+def Xtest_loss():
for delta in [0.0, 0.3]:
print("delta", delta)
# Hit labels
--
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